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Video-based Reconstruction of Animatable Human Characters

Abstract

We present a new performance capture approach that incorporates
a physically-based cloth model to reconstruct a rigged fullyanimatable
virtual double of a real person in loose apparel from
multi-view video recordings. Our algorithm only requires a minimum
of manual interaction. Without the use of optical markers
in the scene, our algorithm first reconstructs skeleton motion and
detailed time-varying surface geometry of a real person from a reference
video sequence. These captured reference performance data
are then analyzed to automatically identify non-rigidly deforming
pieces of apparel on the animated geometry. For each piece of apparel,
parameters of a physically-based real-time cloth simulation
model are estimated, and surface geometry of occluded body regions
is approximated. The reconstructed character model comprises
a skeleton-based representation for the actual body parts and
a physically-based simulation model for the apparel. In contrast to
previous performance capture methods, we can now also create new
real-time animations of actors captured in general apparel.